An Interview with Reviewer #2

Peer review, the cherished academic tradition of having your work criticized by anonymous angry people, is an excellent chance for you to see your prose violated in public. According to one publisher, peer review also helps "increase networking opportunities," in that after having your paper reviewed you will become very, very interested in finding out the names and addresses of all your reviewers.

The reviewing panel consists of two or three reviewers, known by their pseudonyms "Reviewer #1," "Reviewer #2," "Johnny Two-Knuckles," "Icepick Willie," and so on. As everyone knows, the review process tends to be a "good-cop, bad-cop" routine, with Reviewer #1 being nice and lenient - pointing out you shouldn't use Comic Sans font, for instance - while Reviewer #2 is so offended to read your paper that he thinks you should, in so many words, die.

Reviewer #2 is in fact a man named Gary who owns a hardware store in Winnipeg. Although no longer in academia, Gary is still the man who can be counted on, when the chips are down, to write a scathing review of whatever he's reading. Editors scramble to recruit Gary when confronted with a paper they may have to accept, and he is regularly solicited for freelance reviewing. This week we caught up with Gary at his summer home on the banks of Lake Manitoba.

===============

Andy's Brain Blog: How did you become interested in reviewing?

Gary: I took a psychology class in college where we critiqued each other's class projects that were written like scientific articles. Then we anonymously reviewed each other's papers. The professor was impressed that I managed to reject every single paper that I read, and that I also managed to make unnecessary remarks about the author's intelligence and work ethic. At the time I didn't even know what rejecting a paper meant. It just came naturally to me. He put in a good word for me at Elsevier.

ABB: And what happened then?

Gary: Well, I began reviewing everything I read. One time I got so into it that I ended up reviewing the back of a cereal box. It was an accident, but the review was accepted anyway. Two employees at General Mills got fired because of it.

ABB: Wow.

Gary: Yeah. There were grammatical mistakes on there like you wouldn't believe. I couldn't follow the logic of how solving a word game would help Buzz escape from a bank vault full of honey. And the figures were atrocious.

ABB: What was the most memorable review you ever did?

Gary: It's funny you ask, because just last week I returned from the annual Reviewer's Gala in Manhattan. It's a private party for those who have the highest rate of rejecting manuscripts, with awards given for achievements like Most Papers Rejected, Most Brutal Review, Most Irrelevant Comment, and so on. This year I won the prize for Most Hurtful Comment, which went something like: "Writing this paper didn't make you a terrible scientist - you were born one." When the emcee read that line, the audience went wild.

ABB: What is the most ridiculous comment you've ever gotten someone to address?

Gary: I'm not that good at making crazy requests, but one of my fellow reviewers - Carl - can get people to do almost anything. One of his comments was, and I quote: "This is a strong paper, but I think it would be even stronger if, for some reason, all of the authors did the gallon challenge, and uploaded a video of it to YouTube. Now obviously you don't have to do this, but you should, because I am a reviewer."

ABB: They actually did that?

Gary: Yeah. One of them had to go to the hospital. Carl felt pretty bad about that one.

ABB: What advice would you give to a first-time reviewer?

Gary: Rejecting a paper takes a tremendous amount of courage. We've all had the temptation to accept a paper because the science was "solid," or because the logic was "air-tight," or because one of the authors secretly gave us "money." Be firm! I find that I write my best reviews when I'm pissed off about something that has nothing to do with the paper, such as getting something in the mail about taxes.

ABB: You owe a lot of taxes?

Gary: No, I just found out about them, as a concept. They're ridiculous. That's the kind of thing I'm talking about that will get you in the right mood to review a paper.

ABB: Have you ever accepted a paper?

Gary: No.

ABB: Never?

Gary: Never-ever.

ABB: Never even come close?

Gary: Well, there have been a few times. Maybe if one of the authors had the same last name as a celebrity I like, such as Barry Manilow or Kenny G. But other than that, no.

ABB: How long does it take you to write a review?

Gary: Not long. I have a template that I follow, which is a lot like Mad-Libs. For example, "This is an interesting [study / review / prophecy], but I find the [results / figures / theology] unconvincing because I am [an expert / a skeptic / a nun]." Things like that.

ABB: So, how long does it take to get back to the authors? A couple of days? A week?

Gary: No, no, nothing like that. The review takes a couple of days at the most, but you can't let the editor think that you're just blowing through it. I sit on it for at least a few months.

ABB: What are your strategies for writing a review? Is it to always go negative, or what?

Gary: Well, you have to be careful about that. Writing only negative comments raises suspicions that you're taking out your own frustrations and lack of success on the authors instead of addressing their arguments. I aim for a mix of negative comments, nitpicking, and vague sentences. Vague sentences are great, because the authors aren't going to admit that they don't understand what you're saying. Asking an academic to be clear is like asking him to take his clothes off - it's a rude request, almost obscene. So instead they reply as though they understood perfectly what you were saying. It's amazing to see how they try to interpret what is in fact nonsense.

ABB: Can you give an example?

Gary: Sure. Let me see - here's one: "Among the considerations that arise at this stage are the likelihood that the manuscript would seem of considerable interest to those working in the same area of science and the degree to which the results will stimulate new thinking in the field, although we cannot be persuaded of the justifiability, synergy, or translatability of how these results integrate with the conclusions and narrative of Fensterwhacker et al, 2009. Are you professional. Also, you spelled 'their' wrong (should be 'they're': p. 19)."

ABB: I have no idea what that means.

Gary: Exactly.

ABB: How do they respond?

Gary: Usually they begin with something like "We thank the reviewer for their insightful comment," or "We are just thrilled by this excellent suggestion," or "I simply cannot wait to meet this reviewer in person and show him how incredibly, insanely grateful I am, which in no way would include kidnapping his dog." It's interesting how far someone will bend over backwards to address a comment that could've been written by a complete space loon.

ABB: Why do you keep doing this? You're not in academia anymore.

Gary: I try to focus on the big picture. I think that by irritating so many people, everybody will have something in common to talk about. Then they can bond over their shared frustrations and challenges. It makes academia more like a family, except in the sense of being related to or liking or caring about one another.

ABB: Gary, thanks for your time.

Gary: You spelled "your" wrong.

New Website

Readers have complained I haven't updated in a while. Do you know why I haven't updated? Too much Andy's Brain Blog is bad for you. It's like cigarettes, booze, or Nutella. It should be enjoyed in moderation - if at all.

Many of you probably felt that the writing here was slowly petering out. I don't blame you. I've come across sites like that - sites that make me feel as though I'm walking through an abandoned house. What's unsettling is that the writing didn't end; it stopped. That makes me think something terrible happened to the author. Maybe he said everything he had to say; maybe he lost interest; maybe he simply lost inspiration - and couldn't bear to look at those half-stitched monstrosities he began but never finished. I understand. There are many posts that I began to write, but then abandoned - they didn't sound right. You would be surprised how many of these limbless horrors I have buried in my graveyard.

There are two other reasons why I haven't written. One, long periods of absence tend to filter out the fair-weather readers and leave me with only the fanatics. Two, I have been building a new website - a professional website, complete with photos of me doing professional things, such as posing for the camera. I felt that it was time to move; some may disagree. I hate to disappoint them.

Regardless, my posts will continue on the new website; and, to smooth the transition, new writings will be posted to both sites for the next few months. I haven't decided yet what I'll do with this blog; I am too fond of it to simply press "delete" and see it vanish into the electricity. There's history here. Perhaps I'll write something here once in a while with my more unprofessional thoughts. I don't intend to stop anytime soon.

Yet I know that, whatever happens to me, there are others who carry the flag; that there are others who are doing what I do. A few examples come to mind: Mumford Brain Stats; Crash Log; Diffusion Imaging. And that is why this blog, being what it is - a desire to help you understand, to get you excited about neuroimaging; above all, to make you see - will survive even if it die.

fMRI Power Analysis with NeuroPower



One of my biggest peeves is complaints about how power analyses are too hard. I often hear things like "I don't have the time," or "I don't know how to use Matlab," or "I'm being held captive in the MRI control room by a deranged physicist who thinks he is taking orders from the Quench button."

Well, Mr. Whiny-Pants, it's time to stop making excuses - a new tool called NeuroPower lets you do power analyses quickly and easily, right from your web browser. The steps are simple: Upload a result from your pilot study, enter a few parameters - sample size, correction threshold, credit card number - and, if you listen closely, you can hear the electricity of the Internet go booyakasha as it finishes your power analysis. Also, if a few days later you notice some odd charges on your credit card statement, I know nothing about that.

The following video will help you use NeuroPower and will answer all of your questions about power analysis, including:

  • What is a power analysis?
  • Why should I do a power analysis?
  • Why shouldn't I do a power analysis on a full dataset I already collected?
  • How much money did you spend at Home Depot to set up the lighting for your videos?
  • What's up with the ducks? "Quack cocaine"? Seriously?

All this, and more, now in 1080p. Click the full screen button for the full report.


Getting Started with E-Prime, Chapter 1: Objects


When I first opened up E-Prime, the experiment builder, I was struck by how colorful it was: the thumbnails of flags, hourglasses, computer screens; the blocks of code in shades of blues, greens, and grays; and, especially charming, the outline of a little purple man running, a cute way to represent the "run" button - yet to develop associations of errors, troubleshooting, and failure. Always watching, always waiting, perpetually frozen in running profile, was the little purple man, future inhabitant of future nightmares.

All of these colors led me to believe that E-Prime was a friendly software package for the non-programmer, and that it would definitely not lead to feelings of worthlessness, frustration, and eating an entire Sara Lee cake. However, I was proved wrong when I had to make an experiment more complicated than displaying the words "Hello," "Goodbye," and possibly loading a video file of a dog burping. (This can be found with your E-Prime installation under My Experiments/DogBurp_Demo.es2.) Which is how I came upon the E-Prime documentation.

The documentation for E-Prime is big. I recommend printing it, stapling it together with an industrial-sized Kirkland stapler from Costco, and feeling its heft. Or, if you prefer not to print it, open it up in a word processor and see how the scrollbar shrinks to the size of a tic-tac. In both cases the feeling is the same: This thing is Big. Huge. Biggest thing ever.

And then there are the words - the words! Words like object, procedure, list; context, attribute, trial; dimension, property, sphincter, photosynthesis. For someone with no coding background, this is a strange argot - words familiar in everyday life, but hopelessly confusing when trying to build an experiment. No wonder so many graduate students lose faith, drop out of school, and, instead of pursuing the invigorating career of a researcher spending the majority of his life in front of a computer, they wind up in some dull, unexciting job, such as professional gambler or hitman.

I don't want you to suffer the same fate. This is the start of my own documentation for E-Prime: An alternative to the Youtube videos posted by PST, the company that spawned E-Prime, and its bastard, slack-titted gorgon half-sister, E-Basic. I find those videos well-meaning and sometimes informative, but incomplete. After all, they were made by the programmers of E-Prime - they didn't have to slave away at it, suffer for it, like you and I did! This is my perspective from the other side; recognizing the typical pitfalls awaiting a new programmer and how to avoid them, along with how to make E-Prime submit to your will. The solutions are not always elegant; the coding will infuriate; but, if you watch the videos, you just might get the answers you need. No school this afterlunch, but education certain, with Andy as teacher.


AFNI Install on OS X El Capitan

It's been two and half years (three, if you round up) since I uploaded the first videos about AFNI: How to install it, how to run it, how to make it fulfill all your wildest dreams, which may or may not include taking a dollop of Nutella, drying it in the sun, grinding it into a fine powder, and then snorting it.

I was in Rochester for three weeks in July, lodged at a fly-blown hostel. During the days I would talk with researchers and go to meetings and help design studies, but found myself more often on the benches of Eastman Quadrangle, lazily swatting at mosquitoes and feeling the burst of their rubescent abdomens against my skin. I would sit outside where in the distance you could see a church with large oval stained glass and I would think about nothing in particular. The weather in the mornings was perfect, and I took my exercise down by the Erie canal, up around Cobbs Hill Reservoir, and through neighborhoods I didn't know. In the evenings I would make my way downtown for music at the Eastman School and barbecue on the Genesee. And in those nights I worked, obsessively, on those tutorials and videos whose fate had somehow become strangely entangled with mine. Who knew who was reading, who was watching? There was something of an inverted voyeuristic pleasure in thinking about it.

And now here I am, three years later, promoted to seedy manhood and reminiscing of towns and cities; those icy runs on the country lanes of Northfield in windchills of forty below; going to the piano rooms of the Wexner center to practice Scriabin etudes and my beloved, immortal Waldstein; running around Woodlawn field, each loop zero point four-two miles, running ten, twenty, thirty loops at a time, hoping that enough physical exertion would work off a serious infection of heartsickness; crossing the finish line in Indianapolis with burning lungs and knotted calves, surrounded by the vomit of fellow runners, the clock just a shade under two hours and thirty minutes, feeling something break inside me and knowing it was the end of something.

Such were the days, comrades - and how the days have changed! All that I did back then was well enough for its time; but as new wine requires new bottles, so do new operating systems require new installation instructions; and it has come to my attention that several AFNI users are having issues, both technically and emotionally, with getting AFNI to cooperate with Macintosh's newest OS, El Capitan (which is Spanish for, "The Capitan.")

Sensing that urgent action was needed, I turned on my computer, booted up my web browser, and sat with my posterior firmly planted on my chair until somebody else fixed the problem. Fortunately this was not long in coming, and Pete Molfese over at Crash Log has documented how to do it; however, as I am sensitive to how long it takes to click on links, I have reproduced all of the instructions here in full, along with a helpful video which maybe includes a special effect involving my sports jacket.

Here are the steps:

  1. Install XQuartz from xquartz.org (Allows GUIs to run from Unix shells; the "X" symbol that pops up in your dock when you first run AFNI)
  2. Install XCode from the Apple Store
  3. Install Homebrew (a package manager for Mac) using the following command:
    1. For bash: ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
    2. For tcsh: curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install | ruby
    3. Use homebrew to get the following:
      1. The GNU Compiler Collection (GCC) with: brew install gcc --with-all-languages --without-multilib
      2. Pyqt, which you'll need for the .py scripts in AFNI: brew install cartr/qt4/pyqt
      3. GLib, low-level libraries that take care of the little things under the hood: brew install glib
      4. Link libgomp to the correct location using the following:
        1. ln -s /usr/local/Cellar/gcc/5.3.0/lib/gcc/5/libgomp.1.dylib /usr/local/lib/libgomp
        2. Note that the version is continually being updated, so this command may change; for example, replace the 5.2.0 part with 5.3.0. Check the path to /usr/local/Cellar/gcc to see if the path exists. If you link the wrong path, rerun the command with the correct path and the -sf option.
        3. Download the latest AFNI package here, or type the following into your terminal (assuming you are installing version 10.7; replace with whatever version you want to download):

cd

mkdir abin

curl -O http://afni.nimh.nih.gov/pub/dist/tgz/macosx_10.7_Intel_64.tgz

 tar -xzf macosx_10.7_Intel_64.tgz

mv macosx_10.7_Intel_64 abin

rm macosx_10.7_Intel_64.tgz

  1. Paste the following commands from the AFNI install webpage into your terminal (for tcsh shell):

echo 'set path = (/usr/local/bin $path $HOME/abin)' >> .cshrc

echo 'setenv DYLD_FALLBACK_LIBRARY_PATH $HOME/abin' >> .cshrc

echo 'setenv PYTHONPATH /usr/local/lib/python2.y/site-packages' >> .cshrc

source .cshrc

rehash

Top Ten Things I Learned in Graduate School (TRIGGER WARNING: Includes Spiro Agnew)

"It is a duty incumbent on upright and creditable men of all ranks who have performed anything noble or praiseworthy to record in their own words the events of their lives. But they should not undertake this honorable task until they are past the age of forty."

-Benvenuto Cellini, opening sentence of his Autobiography (c. 1558)


  1. Date within your cohort! Or not. Either way, you'll have a great time! Maybe.
  2. If you have more than ten things to say, you can make a longer list.
  3. If you rearrange the letters in the name "Spiro Agnew," you can spell "Grow A Penis." Really? Really.
  4. Think of teaching a class as a PG-13 movie: to keep the class titillated and interested, you're allowed to make slightly crude references without being explicit; and, if you want, you're entitled to say the f-word ("fuck") once during the semester.
  5. When they say, "Don't date your students until the class is over," they mean when the semester is over, not just when classtime is over.
  6. Virtually everyone who throws around the word "sustainable" has no idea what they're talking about, unless it's that water situation in California. Things are seriously f-worded over there.
  7. If you come into graduate school not knowing how to code, teach yourself. Only after getting frustrated and making no headway, only after you have exhausted every avenue of educating yourself - only then is it acceptable to find someone else to do it for you, and then take credit for it. You gotta at least try!
  8. You know you've been doing neuroimaging analysis for a long time when you don't think twice about labeling a directory "anal." Ditto for "GroupAnal."
  9. You know you've been in graduate school too long when you can remember the deidentification codes for all of your subjects, but not necessarily the names of all of your children.
  10. When you first start a blog in graduate school, everything you write is very proper and low-key, in the fear that you may offend one of your colleagues or a potential employer. Then after a while you loosen up. Then you tighten up again when you're on the job market. Then you get some kind of employment and you loosen up again. And so on.
  11. When I first started blogging, I figured that people would take the most interest in essays that I had taken considerable pains over, usually for several days or weeks. Judging from the amount of hits for each post, readers seem to vastly prefer satirical writings about juvenile things such as "the default poop network," and humorous neuroimaging journal titles with double entendres - silly crap I dashed off in a few minutes. Think about that.
  12. The whole academic enterprise is more social than anything. It sounds obvious and you will hear it everywhere, but you never appreciate it until you realize that you can't just piss off people arbitrarily and not suffer any consequences somewhere down the line. Likewise, if you are good to people and write them helpful blog posts and make them helpful tutorial videos, they are good to you, usually. Kind of like with everything else in life.
  13. If you get a good adviser, do not take that for granted. Make every effort to make that man's life easier by doing your duties, and by not breaking equipment or needlessly stabbing his other graduate students. By "good adviser" I mean someone who is considerate, generous with his time and resources, and clear about what you need to do to get your own career off the ground while giving you enough space to develop on your own. I had such an adviser, and that is a big part of the reason that the past five years of my life have also been the best five years. That, and the fact that I can rent cars on my own now.

Have you finished graduate school and are now in a slightly higher paying but still menial and depressing job, and would like to share your wisdom with the newer generation of young graduate students? Can you rent a car on your own now? Did you try the Spiro Agnew Anagram Challenge (SAAC)? Share your experiences in the comments section!

SPM Smoothing: A Reader Writes

The angry red pustule of the Gaussian normal distribution
I love questions, because questions get answers. One question you may be asking is, "Why are my smoothness estimates in SPM so whack?" To which the obvious response is, "How much whack are we talking about here? Whiggidy-whack, or just the regular kind?" Details matter.

If the former, then the following code snippet may help. In the absence of a gold standard for calculating smoothness estimates, often we have to resort to our own ingenuity and cunning, by which I mean: Copy what other people are doing. One alert reader, Tamara, noticed that the standard SPM function for estimating smoothness, spm_est_smoothness, is so whack that all the other SPM functions want nothing to do with it. Which is kind of the goal of life, when you think about it - to not be that guy everyone else wants to avoid.

In any case, if you are having issues with it, the following code may help. I've also included the rest of the email, just to make you aware that I can and will publish your correspondence without your consent.


Hi Andy, I never figured out why spm_est_smoothness is not working, although other people have had the same issue with getting estimates in the thousands.  Ultimately, I ended up using this simple code to estimate each individual's smoothness, and then averaged across subjects.  Jim Lee posted this on the SPM listserv along with this note:  
The smoothness estimates in SPM.xVol.FWHM are in units of VOXELS, so you need to multiply by the voxel dimensions to get them in mm. Something like this:
load SPM.mat; M = SPM.xVol.M; VOX = sqrt(diag(M(1:3,1:3)'*M(1:3,1:3)))'; FWHM = SPM.xVol.FWHM; FWHMmm= FWHM.*VOX; disp(FWHMmm);

Thanks again for your help!!

Mumford & Stats: Up Your Neuroscience Game


Jeanette Mumford, furious at the lack of accessible tutorials on neuroimaging statistics, has created her own Tumblr to distribute her knowledge to the masses.

I find examples like these heartening; researchers and statisticians providing help to newcomers and veterans of all stripes. Listservs, while useful, often suffer from poorly worded questions, opaque responses, and overspecificity - the issues are individual, and so are the answers, which go together like highly specific shapes of toast in a specialized toaster.* Tutorials like Mumford's are more like pancake batter spread out over a griddle, covering a wide area and seeping into the drip pans of understanding, while being sprinkled with chocolate chips of insight, lightly buttered with good humor, and drizzled with the maple syrup of kindness.

I also find tutorials like these useful because - let's admit it - we're all slightly stupid when it comes to statistics. Have you ever tried explaining it to your dad, and ended up feeling like a fool? Clearly, we need all the help we can get. If you've ever had to doublecheck why, for example, a t-test works the way it does, or brush up on how contrast weights are made, this website is for you. (People who never had to suffer to understand statistics, on the other hand, just like people who don't have any problems writing, are disgusting and should be avoided.)

Jeanette has thirty-two videos covering the basics of statistics and their application to neuroimaging data, a compression of one of her semester-long fMRI data courses which should be required viewing for any neophyte. More recent postings report on developments and concerns in neuroimaging methods, such as collinearity, orthogonalization, nonparametric thresholding, and whether you should date fellow graduate students in your cohort. (I actually haven't read all of the posts that closely, but I'm assuming that something that important is probably in there somewhere.) And, unlike myself, she doesn't make false promises and she posts regularly; you get to stay current on what's hot, what's not, and, possibly, you can begin to make sense of those knotty methods sections. At least you'll begin to make some sense of the gibberish your advisor mutters in your general direction the next time he wants you to do a new analysis on the default pancake network - the network of regions that is activated in response to a contrast of pancakes versus waffles, since they are matched on everything but texture.**

It is efforts such as this that make the universe of neuroimaging, if not less complex, at least more comprehensible, less bewildering; more approachable, less terrifying. And any effort like that deserves its due measure of praise and pancakes.


*It was only after writing this that I realized you put bread into a toaster - toast is what comes out - but I decided to just own it.

**Do not steal this study idea from me.

Neuroimaging Training Program Postmortem




Imagine cramming thirty-five intelligent, motivated, enthusiastic, good-smelling individuals into a large bottle, adding in over twenty incredible speakers, tossing in a few dozen MacBooks and several gallons of boiling hot coffee, and shaking it up using an industrial-sized can shaker. (These things must exist somewhere.)

The screaming mass of coffee-scalded and MacBook-concussed individuals would look a lot like the group that descended upon UCLA like a swarm of locusts, hungry for knowledge and even hungrier for the prestige of attending the Neuroimaging Training Program. Sure, there's all the knowledge and everything, but let's get real - it's all about the hardware: Rollerball pens, pins for the lapel of your sports jacket, and decal drinking glasses.

But the workshop was pretty good as well. One colleague asked me what the zeitgeist was like; what researchers are focusing on, concerned about, looking forward to. Here's a list that I came up with:


  1. The funding environment in this country is awful, broken, and offers perverse incentives to carry out underpowered studies that are difficult and sometimes impossible to replicate, eroding the very foundation of science and undermining humanity's pursuit of truth.
  2. We need to find a way to get more of that grant money, nahmsayin.
  3. Anybody who runs a correlation study with less than a hundred subjects is scum.


In addition, there appears to be a shift toward data-driven techniques, whereby you use your data, which everybody agrees was mostly crap to begin with, to carry out statistical learning tests. This includes classification techniques such as ICA, k-means clustering, and MVPA (pronounced "muhv-pah"). Of these, MVPA is the most popular in neuroimaging analysis, given its snappy acronym and the crackerjack idea that distributed patterns of activation can yield something interpretable after all of your other univariate approaches have failed miserably. There is also a new toolbox out, The Decoding Toolbox, that provides a remarkable visualization of how MVPA works, and may well be the subject of future tutorials; which, based on my glacial pace, may be well into Donald Trump's fourth or fifth presidential term.

Speaking of slow paces, I should probably stop being cute and come out with it - I didn't do what I said I was going to do: provide regular updates on what was going on at the workshop. I began with the best of intentions (truly, gentlemen, I did!); but I quickly realized that many of the posts forming in my head were boring, boy scout recapitulations of what was going on day to day; in short, information that any curious person could get from the website. This, coupled with an engaging group of people that I spent all my days and nights with, swapping ribald stories and interesting ideas, hacking away at projects and whiling away my evenings in downtown Westwood, sapped the motivation to write alone in my room.


But now I am back, and many of the ideas put in cold storage the past few weeks have bubbled again to the surface. For example: Many people (myself included) have an imperfect understanding of how to teach neuroimaging. I saw very good examples from some of the speakers at the workshop (as well as some bad examples), and it made me think: How to best pitch this stuff to both beginners and veterans? The same thing I've been working on, by and by, for the past three years, never to my satisfaction. A few of the students I teach privately have given me some insight into common stumbling blocks to understanding, as well as what explanations or images (often bizarre or titillating) work best.

There were many ideas, tools, approaches discussed at the workshop; all of them intriguing, many of them dazzling, none of them immediately accessible. To build that bridge between those ideas and the researchers who need them - a six-lane bridge, both ways, with the elevator thingy that lifts up to let ships go underneath - is my goal. Talk is cheap, and not everyone keeps their promises; but to attempt it, to refuse to simply fade away in a pathetic morendo, and instead dare to fail spectacularly - I'm talking Hammerklavier first chord daredevil-leap-of-faith here - is a worthy pursuit. Let us all hope, especially for my sake, that it is a profitable one as well.

Neuroimaging Training Program: Days 1 & 2

The first two days of NiTP have been intense - MRI physics, neurobiology, experimental design, plus much eating out, have all been crammed down our faces, only to be slowly digested over the next two weeks to form the hard bolus of wisdom, and then regurgitated back onto our colleagues when we return home. (Maybe not the best metaphor, but I'm sticking with it.)

Much of the lectures were mostly review, but useful review, and delivered by an array of brilliant scientists who, if they chose to, could easily be doing something much more sinister with their intellectual powers, such as creating a race of giant acid-spewing crabs to paralyze the world in fear. I'm sure the thought has passed through their minds at some point. Fortunately for us, however, they are content to devote their energies to progress the field of neuroimaging. And while you can find their slides and audio lectures online here (plus a livestream over the next couple of weeks here), I'll try my best to intermittently summarize what we've done so far. This is mainly a brief information dump; some of these I'll try to develop upon once I get back to New Haven.


  • After a brief introduction and overview by MR physicist Mark Cohen, we then listened to a keynote speech by Russ Poldrack, who told us the various ills and pitfalls of neuroimaging and cognitive neuroscience, including inflated effect sizes, poor reproducibility, and how shoddy experimental design leads to ever-withering claims of neophrenology. We each mentally swore to never again engage in such scurrilous practices, while continuing to have the nagging feeling somewhere in the back of our mind, that we'd compromise at some point. It's like telling a man not to use his fingers to scrape the last streaks of Nutella from the bottom of the jar; you can't ask the impossible all the time.
  • Next up was a refresher on neurons, neurobiology, and the Blood Oxygenation Level Dependent (BOLD) response. With hundreds of billions of tiny neurons crammed inside our cranium, along with a complex network of glia, dendrites, synapses, and vesicles, it's a miracle that the thing works at all. Couple this with an incredibly quick electrical and chemical process generating action potentials and intricate relationships between metabolic function of the cell and hemodynamics delivering and shuttling blood to and from activation sites, and you begin to question whether some of the assumptions of FMRI are all that robust - whether it truly measures what we thinks it's measuring, or just some epiphenomena of neural activity steps removed from the actual source.
  • But we all have to keep that grant money flowing somehow, which is where experimental design comes in, smoothly eliding over all those technical concerns with a sexy research question involving consciousness, social interaction, or the ever-elusive grandmother neuron. However, no research question is immune to sloppy design, or asking ourselves whether the same question can be answered much more easily, and much more cheaply using a behavioral paradigm. Once you have a good neuroimaging research question, however, you also need to question several of the assumptions going into the design; such as whether the assumption of pure insertion holds - whether adding in another cognitive process leads to activity only sensitive to that process, without any undesired interactions - and potential stimuli confounds.
  • Lastly, we covered data preprocessing and quality control, in particular the vicissitudes of head motion and why humans are so stubborn in doing things like moving, breathing, making their hearts beat, and other things which are huge headaches for the typical neuroimager to deal with. We're not asking for much here, guys! Several of these issues can be resolved either by excluding acquisitions contaminated by motion or other sources of intrinsic noise, or, more commonly, modeling them so that any variance gets assigned to them and not to any regressors that you care about. Another related topic was using a Matlab function coded by Martin Monti to assess any multicollinearity in your design, which I plan to cover in detail in a future post. You can find the code on the NiTP website.
  • Oh, and k-space. We talked about k-space. I've encountered this thing off and on for about seven years now, and still don't completely understand it; whenever I feel as though I'm on edge of a breakthrough to understand it, it continues to elude me. Which leads me to conclude that either, a) I just don't understand it, or b) nobody else understands it either and it's really meaningless, but enough people have invested enough into it to keep up the charade that it's continued to be presented as a necessary but abstruse concept. For the sake of my self-esteem, I tend to believe option b.
That's about it! I'll plan on posting a couple more updates throughout the week to keep everyone abreast of what's going on. Again, check out the livestream; you're seeing and hearing the same things I am!